Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder
Igneous rocks exhibit significant variations in mineral content due to differences in magma types and the environment in which they solidify, and the skeleton parameters of different lithology are obviously different. The determination of mineral content of the rock matrix is an important task in ev...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Well Logging Technology
2024-08-01
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| Series: | Cejing jishu |
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| Online Access: | https://www.cnpcwlt.com/#/digest?ArticleID=5614 |
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| author | JIA Ruilong PAN Baozhi WANG Qinghui LI Yan GUAN Yao WANG Xinru |
| author_facet | JIA Ruilong PAN Baozhi WANG Qinghui LI Yan GUAN Yao WANG Xinru |
| author_sort | JIA Ruilong |
| collection | DOAJ |
| description | Igneous rocks exhibit significant variations in mineral content due to differences in magma types and the environment in which they solidify, and the skeleton parameters of different lithology are obviously different. The determination of mineral content of the rock matrix is an important task in evaluating reservoirs, which is of great significance in stratigraphic lithology division, calculation of matrix parameters and study of depositional environments. In this study, a predictive model for mineral content in igneous rocks is proposed. The model utilizes data from 17 elements obtained through element logging. It employs a VAE (Variational AutoEncoder) approach to predict mineral content and reconstruct the elemental weight content. The model validation reveals that the proposed model has a smaller mean absolute error and mean square error compared to three typical methods: BP (Back Propagation) neural networks, ridge regression and support vector machines. Furthermore, the model is applied to a section of buried hill igneous rock well in the South China Sea. The results demonstrate the superiority of the proposed model over the typical algorithms while maintaining good applicability. |
| format | Article |
| id | doaj-art-a0fb884e18754ce1bbeedfa3ce454e82 |
| institution | OA Journals |
| issn | 1004-1338 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Editorial Office of Well Logging Technology |
| record_format | Article |
| series | Cejing jishu |
| spelling | doaj-art-a0fb884e18754ce1bbeedfa3ce454e822025-08-20T01:55:26ZzhoEditorial Office of Well Logging TechnologyCejing jishu1004-13382024-08-0148440741510.16489/j.issn.1004-1338.2024.04.0011004-1338(2024)04-0407-09Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoderJIA Ruilong0PAN Baozhi1WANG Qinghui2LI Yan3GUAN Yao4WANG Xinru5College of Geoexploration Science and Technology Jilin University, Changchun, Jilin 130026, ChinaCollege of Geoexploration Science and Technology Jilin University, Changchun, Jilin 130026, ChinaShenzhen Branch of CNOOC (China) LTD., Shenzhen, Guangdong 518054, ChinaCollege of Geoexploration Science and Technology Jilin University, Changchun, Jilin 130026, ChinaShenzhen Branch of CNOOC (China) LTD., Shenzhen, Guangdong 518054, ChinaCollege of Geoexploration Science and Technology Jilin University, Changchun, Jilin 130026, ChinaIgneous rocks exhibit significant variations in mineral content due to differences in magma types and the environment in which they solidify, and the skeleton parameters of different lithology are obviously different. The determination of mineral content of the rock matrix is an important task in evaluating reservoirs, which is of great significance in stratigraphic lithology division, calculation of matrix parameters and study of depositional environments. In this study, a predictive model for mineral content in igneous rocks is proposed. The model utilizes data from 17 elements obtained through element logging. It employs a VAE (Variational AutoEncoder) approach to predict mineral content and reconstruct the elemental weight content. The model validation reveals that the proposed model has a smaller mean absolute error and mean square error compared to three typical methods: BP (Back Propagation) neural networks, ridge regression and support vector machines. Furthermore, the model is applied to a section of buried hill igneous rock well in the South China Sea. The results demonstrate the superiority of the proposed model over the typical algorithms while maintaining good applicability.https://www.cnpcwlt.com/#/digest?ArticleID=5614igneous rockmineral contentvae (variational autoencoder)element logging |
| spellingShingle | JIA Ruilong PAN Baozhi WANG Qinghui LI Yan GUAN Yao WANG Xinru Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder Cejing jishu igneous rock mineral content vae (variational autoencoder) element logging |
| title | Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder |
| title_full | Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder |
| title_fullStr | Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder |
| title_full_unstemmed | Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder |
| title_short | Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder |
| title_sort | method for determining igneous rock mineral content using element logging data based on variational autoencoder |
| topic | igneous rock mineral content vae (variational autoencoder) element logging |
| url | https://www.cnpcwlt.com/#/digest?ArticleID=5614 |
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